• 한국지능시스템학회논문지
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• 제18권6호
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• pp.863-870
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• 2008

본 논문에서는 관측왜곡을 포함하고 있는 적응형 순항제어 시스템개발에 필수적인 필터링 방식에 대한 연구를 진행한다. 앞선 차량의 정확한 추적과 의도파악을 위하여 기본적으로 IMM (Interacting multiple model)을 사용하며 관측의 왜곡을 보상하기 위하여 확률적 퍼지 모델을 세안한다. 확률적 퍼지 모델은 기존의 결정형 퍼지모델과 달리 모델링 오차를 확률로 모델링한다. 끝으로 확률퍼지모델과 IMM을 결합한 FIMM (Fuzzy IMM)을 제안하여 관측왜곡이 발생하는 레이더를 이용한 전방차량의 추적 알고리즘을 제안한다.

• 제어로봇시스템학회논문지
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• 제7권5호
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• pp.445-453
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• 2001

The multiple targets tracking problem has been one of the main issues in the radar applications area in the last decade. Besides the standard Kalman filtering, various methods including the variable dimen-sion filter, input estimation filter, interacting multiple model(IMM) filter, dederated variable dimension filter with input estimation, etc., have proposed to address the tracking and sensor fusion issues. In this pa- per, two existing tracking algorithm, i.e, the IMM filter and the variable dimension filter with input estima-tion(VDIE), are combined for the purpose of improving the tracking performance for maneuvering targets. To evaluate the tracking performance of the proposed algorithm, three typical maneuvering patterns, i.e., waver, pop-up, and high-diver motions, are defined and are applied to the modified IMM filter as well as the standard IMM filter. The smaller RMS tracking errors, in position and velocity, of the modified IMM filter than the standard IMM filter are demonstrated though computer simulations.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.943-945
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• 1996

In this paper, we present an algorithm that allows tracking of a target using measurements obtained from a sensor with limited resolution. The Interacting Multiple Model (IMM) algorithm has been shown to be one of the most cost-effective estimation schemes for hybrid systems. The approach consists of IMM algorithm combined with a coupled version of the Joint Probabilistic Data Association Filter for the target that splits into two targets.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2006년도 춘계학술대회 학술발표 논문집 제16권 제1호
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• pp.425-428
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• 2006

In this paper, we propose an interacting multiple model (IMM) method using intelligent tracking filter with fuzzy gain to reduce tracking errors for maneuvering targets. In the proposed filter, to exactly estimate for each sub-model, we propose the fuzzy gain based on the relation between the filter residual and its variation. To optimize each fuzzy system, we utilize the genetic algorithm (GA). Finally, the tracking performance of the proposed method is compared with those of the adaptive interacting multiple model (AIMM) method and input estimation (IE) method through computer simulations.

• International Journal of Control, Automation, and Systems
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• 제4권5호
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• pp.559-566
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• 2006

In the real system application, the interacting multiple model (IMM) based algorithm requires less computing resources as well as a good performance with respect to the various target maneuverings. And it further requires an easy design procedure in terms of its structures and parameters. To solve these problems, a fuzzy interacting multiple model (FIMM) algorithm, which is based on the basis sub-models defined by considering the maneuvering property and the time-varying mode transition probabilities designed by using the mode probabilities as inputs of a fuzzy decision maker, is proposed. To verify the performance of the proposed algorithm, airborne target tracking is performed. Simulation results show that the FIMM algorithm solves all problems in the real system application of the IMM based algorithm.

• International Journal of Control, Automation, and Systems
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• 제5권3호
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• pp.307-316
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• 2007

In real system application, the interacting multiple model (IMM) based algorithm operates with the following problems: it requires less computing resources as well as a good performance with respect to the various target maneuvering, it requires a robust performance with respect to the time-varying measurement noise, and further, it requires an easy design procedure in terms of its structures and parameters. To solve these problems, an adaptive fuzzy interacting multiple model (AFIMM) algorithm, which is based on the basis sub-models defined by considering the maneuvering property and the time-varying mode transition probabilities designed by using the mode probabilities as the inputs of the fuzzy decision maker whose widths are adjusted, is proposed. To verify the performance of the proposed algorithm, a radar target tracking is performed. Simulation results show that the proposed AFIMM algorithm solves all problems in the real system application of the IMM based algorithm.

• International Journal of Control, Automation, and Systems
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• 제6권1호
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• pp.44-53
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• 2008

An interacting multiple model (IMM) estimation algorithm based on the mixing of the predicted state estimates is proposed in this paper for a right continuous jump-linear system model different from the left-continuous system model used to develop the existing IMM algorithm. The difference lies in the modeling of the mode switching time. Performance of the proposed algorithm is compared numerically with that of the existing IMM algorithm for noisy system identification. Based on the numerical analysis, the proposed algorithm is applied to target tracking with a large sampling period for performance comparison with the existing IMM.

• 한국군사과학기술학회지
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• 제18권1호
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• pp.84-92
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• 2015

This paper proposes a new approach to reduce the target estimation error of the measurement angle, especially applied to the medium and long range surveillance radar. If the target has no maneuver and no change in heading direction for a certain time interval, the predicted angle of interacting multiple model(IMM) from the previous track information can be used to reduce the angle estimation error. The proposed method is simulated in 2 scenarios, a scenario with a non-maneuvering target and a scenario with a maneuvering target. The result shows that the new fusion solution(weighted IMM) with the predicted azimuth and the measured azimuth is worked properly in the two scenarios.

• 한국항행학회논문지
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• 제23권3호
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• pp.221-227
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• 2019

본 논문에서는 위성 항법 해를 이용하여 INS의 순수항법을 보상하는 INS / GPS 통합 항법 알고리즘을 구성할 때 불안정한 위성 항법 위치 해 출력에도 안정적인 항법 성능을 보장할 수 있는 IMM (interacting multiple model)필터를 설계하였다. INS / GPS 통합 항법 시스템 구조 내에 칼만필터를 서브 필터로 하는 IMM 필터 구조를 정의하였다. IMM필터 구성시 서브필터는 2개로 구성하였으며, 각각의 칼만필터는 INS의 오차 방정식으로부터 위치, 속도, 자세, 센서 오차 등으로 구성한 16차의 상태를 정의하고 추가로 위성항법의 유색 잡음(coloured measurement noise)영향으로 2차를 확장하였다. 제안한 IMM 필터의 성능을 확인하기위해 위성 항법에 임의의 오차를 위도와 경도에 삽입하고 필터의 추종성을 확인하는 것으로 성능을 비교 분석하였다. 몬테카를로 시뮬레이션을 100회 수행하여 결과를 RMS로 비교한 결과 제안한 필터 방식이 오차에 대해 안정적이며 빠른 수렴결과를 보이고 있음을 확인할 수 있었다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1278-1282
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• 2003

A new interacting multiple model (IMM) method using intelligent input estimation (IIE) is proposed to track a maneuvering target. In the proposed method, the acceleration level for each sub-model is determined by IIE-the estimation of the unknown acceleration input by a fuzzy system using the relation between maneuvering filter residual and non-maneuvering one. The genetic algorithm (GA) is utilized to optimize a fuzzy system for a sub-model within a fixed range of acceleration input. Then, multiple models are composed of these fuzzy systems, which are optimized for different ranges of acceleration input. In computer simulation for an incoming ballistic missile, the tracking performance of the proposed method is compared with those of the input estimation (IE) technique and the adaptive interacting multiple model (AIMM) method.